Predicting network activity from high throughput metabolomics

A longitudinal analysis of the effects of age on the blood plasma metabolome in the common marmoset, Callithrix jacchus

Primates tend to be long-lived for their size with humans being the longest lived of all primates. There are compelling reasons to understand the underlying age-related processes that shape human lifespan. But the very fact of our long lifespan that makes it so compelling, also makes it especially difficult to study. Thus, in studies of aging, researchers have turned to non-human primate models, including chimpanzees, baboons, and rhesus macaques. More recently, the common marmoset, Callithrix jacchus, has been recognized as a particularly valuable model in studies of aging, given its small size, ease of housing in captivity, and relatively short lifespan. However, little is known about the physiological changes that occur as marmosets age. To begin to fill in this gap, we utilized high sensitivity metabolomics to define the longitudinal biochemical changes associated with age in the common marmoset. We measured 2104 metabolites from blood plasma at three separate time points over a 17-month period, and we completed both a cross-sectional and longitudinal analysis of the metabolome. We discovered hundreds of metabolites associated with age and body weight in both male and female animals. Our longitudinal analysis identified age-associated metabolic pathways that were not found in our cross-sectional analysis. Pathways enriched for age-associated metabolites included tryptophan, nucleotide, and xenobiotic metabolism, suggesting these biochemical pathways might play an important role in the basic mechanisms of aging in primates. Moreover, we found that many metabolic pathways associated with age were sex specific. Our work illustrates the power of longitudinal approaches, even in a short time frame, to discover novel biochemical changes that occur with age.

Sequencing the exposome: A call to action

The exposome is a complement to the genome that includes non-genetic causes of disease. Multiple definitions are available, with salient points being global inclusion of exposures and behaviors, and cumulative integration of associated biologic responses. As such, the concept is both refreshingly simple and dauntingly complex. This article reviews high-resolution metabolomics (HRM) as an affordable approach to routinely analyze samples for a broad spectrum of environmental chemicals and biologic responses. HRM has been successfully used in multiple exposome research paradigms and is suitable to implement in a prototype universal exposure surveillance system. Development of such a structure for systematic monitoring of environmental exposures is an important step toward sequencing the exposome because it builds upon successes of exposure science, naturally connects external exposure to body burden and partitions the exposome into workable components. Practical results would be repositories of quantitative data on chemicals according to geography and biology. This would support new opportunities for environmental health analysis and predictive modeling. Complementary approaches to hasten development of exposome theory and associated biologic response networks could include experimental studies with model systems, analysis of archival samples from longitudinal studies with outcome data and study of relatively short-lived animals, such as household pets (dogs and cats) and non-human primates (common marmoset). International investment and cooperation to sequence the human exposome will advance scientific knowledge and also provide an important foundation to control adverse environmental exposures to sustain healthy living spaces and improve prediction and management of disease.

High-Resolution Metabolomics: Review of the Field and Implications for Nursing Science and the Study of Preterm Birth

Most complex health conditions do not have a single etiology but rather develop from exposure to multiple risk factors that interact to influence individual susceptibility. In this review, we discuss the emerging field of metabolomics as a means by which metabolic pathways underlying a disease etiology can be exposed and specific metabolites can be identified and linked, ultimately providing biomarkers for early detection of disease onset and new strategies for intervention. We present the theoretical foundation of metabolomics research, the current methods employed in its conduct, and the overlap of metabolomics research with other "omic" approaches. As an exemplar, we discuss the potential of metabolomics research in the context of deciphering the complex interactions of the maternal-fetal exposures that underlie the risk of preterm birth, a condition that accounts for substantial portions of infant morbidity and mortality and whose etiology and pathophysiology remain incompletely defined. We conclude by providing strategies for including metabolomics research in future nursing studies for the advancement of nursing science.

New insights into the complex regulation of the glycolytic pathway in Lactococcus lactis. I. Construction and diagnosis of a comprehensive dynamic model

This article and the companion paper use computational systems modeling to decipher the complex coordination of regulatory signals controlling the glycolytic pathway in the dairy bacterium Lactococcus lactis.

Local false discovery rate estimation using feature reliability in LC/MS metabolomics data

False discovery rate (FDR) control is an important tool of statistical inference in feature selection. In mass spectrometry-based metabolomics data, features can be measured at different levels of reliability and false features are often detected in untargeted metabolite profiling as chemical and/or bioinformatics noise. The traditional false discovery rate methods treat all features equally, which can cause substantial loss of statistical power to detect differentially expressed features. We propose a reliability index for mass spectrometry-based metabolomics data with repeated measurements, which is quantified using a composite measure. We then present a new method to estimate the local false discovery rate (lfdr) that incorporates feature reliability. In simulations, our proposed method achieved better balance between sensitivity and controlling false discovery, as compared to traditional lfdr estimation. We applied our method to a real metabolomics dataset and were able to detect more differentially expressed metabolites that were biologically meaningful.

Blood transcriptomics and metabolomics for personalized medicine

Molecular analysis of blood samples is pivotal to clinical diagnosis and has been intensively investigated since the rise of systems biology. Recent developments have opened new opportunities to utilize transcriptomics and metabolomics for personalized and precision medicine. Efforts from human immunology have infused into this area exquisite characterizations of subpopulations of blood cells. It is now possible to infer from blood transcriptomics, with fine accuracy, the contribution of immune activation and of cell subpopulations. In parallel, high-resolution mass spectrometry has brought revolutionary analytical capability, detecting > 10,000 metabolites, together with environmental exposure, dietary intake, microbial activity, and pharmaceutical drugs. Thus, the re-examination of blood chemicals by metabolomics is in order. Transcriptomics and metabolomics can be integrated to provide a more comprehensive understanding of the human biological states. We will review these new data and methods and discuss how they can contribute to personalized medicine.

The effects of age and dietary restriction on the tissue-specific metabolome of Drosophila

Dietary restriction (DR) is a robust intervention that extends lifespan and slows the onset of age-related diseases in diverse organisms. While significant progress has been made in attempts to uncover the genetic mechanisms of DR, there are few studies on the effects of DR on the metabolome. In recent years, metabolomic profiling has emerged as a powerful technology to understand the molecular causes and consequences of natural aging and disease-associated phenotypes. Here, we use high-resolution mass spectroscopy and novel computational approaches to examine changes in the metabolome from the head, thorax, abdomen, and whole body at multiple ages in Drosophila fed either a nutrient-rich ad libitum (AL) or nutrient-restricted (DR) diet. Multivariate analysis clearly separates the metabolome by diet in different tissues and different ages. DR significantly altered the metabolome and, in particular, slowed age-related changes in the metabolome. Interestingly, we observed interacting metabolites whose correlation coefficients, but not mean levels, differed significantly between AL and DR. The number and magnitude of positively correlated metabolites was greater under a DR diet. Furthermore, there was a decrease in positive metabolite correlations as flies aged on an AL diet. Conversely, DR enhanced these correlations with age. Metabolic set enrichment analysis identified several known (e.g., amino acid and NAD metabolism) and novel metabolic pathways that may affect how DR effects aging. Our results suggest that network structure of metabolites is altered upon DR and may play an important role in preventing the decline of homeostasis with age.

Reference Standardization for Mass Spectrometry and High-resolution Metabolomics Applications to Exposome Research

The exposome is the cumulative measure of environmental influences and associated biological responses throughout the lifespan, including exposures from the environment, diet, behavior, and endogenous processes. A major challenge for exposome research lies in the development of robust and affordable analytic procedures to measure the broad range of exposures and associated biologic impacts occurring over a lifetime. Biomonitoring is an established approach to evaluate internal body burden of environmental exposures, but use of biomonitoring for exposome research is often limited by the high costs associated with quantification of individual chemicals. High-resolution metabolomics (HRM) uses ultra-high resolution mass spectrometry with minimal sample preparation to support high-throughput relative quantification of thousands of environmental, dietary, and microbial chemicals. HRM also measures metabolites in most endogenous metabolic pathways, thereby providing simultaneous measurement of biologic responses to environmental exposures. The present research examined quantification strategies to enhance the usefulness of HRM data for cumulative exposome research. The results provide a simple reference standardization protocol in which individual chemical concentrations in unknown samples are estimated by comparison to a concurrently analyzed, pooled reference sample with known chemical concentrations. The approach was tested using blinded analyses of amino acids in human samples and was found to be comparable to independent laboratory results based on surrogate standardization or internal standardization. Quantification was reproducible over a 13-month period and extrapolated to thousands of chemicals. The results show that reference standardization protocol provides an effective strategy that will enhance data collection for cumulative exposome research. In principle, the approach can be extended to other types of mass spectrometry and other analytical methods.

Metabolic Consequences of Chronic Alcohol Abuse in Non-Smokers: A Pilot Study

An alcohol use disorder (AUD) is associated with an increased susceptibility to respiratory infection and injury and, upon hospitalization, higher mortality rates. Studies in model systems show effects of alcohol on mitochondrial function, lipid metabolism and antioxidant systems. The present study applied high-resolution metabolomics to test for these changes in bronchoalveolar lavage fluid (BALF) of subjects with an AUD. Smokers were excluded to avoid confounding effects and compliance was verified by cotinine measurements. Statistically significant metabolic features, differentially expressed by control and AUD subjects, were identified by statistical and bioinformatic methods. The results show that fatty acid and acylcarnitine concentrations were increased in AUD subjects, consistent with perturbed mitochondrial and lipid metabolism. Decreased concentrations of methyl-donor compounds suggest altered one-carbon metabolism and oxidative stress. An accumulation of peptides suggests proteolytic activity, which could reflect altered epithelial barrier function. Two metabolites of possible microbial origin suggest subclinical bacterial infection. Furthermore, increased diacetylspermine suggests additional metabolic perturbations, which could contribute to dysregulated alveolar macrophage function and vulnerability to infection. Together, the results show an extended metabolic consequence of AUD in the bronchoalveolar space.

MetabNet: An R Package for Metabolic Association Analysis of High-Resolution Metabolomics Data

Liquid-chromatography high-resolution mass spectrometry provides capability to measure >40,000 ions derived from metabolites in biologic samples. This presents challenges to confirm identities of known chemicals and delineate potential metabolic pathway associations of unidentified chemicals. We provide an R package for metabolic network analysis, MetabNet, to perform targeted metabolome-wide association study of specific metabolites to facilitate detection of their related metabolic pathways and network structures.

Application of gas chromatography/flame ionization detector-based metabolite fingerprinting for authentication of Asian palm civet coffee (Kopi Luwak)

Development of authenticity screening for Asian palm civet coffee, the world-renowned priciest coffee, was previously reported using metabolite profiling through gas chromatography/mass spectrometry (GC/MS). However, a major drawback of this approach is the high cost of the instrument and maintenance. Therefore, an alternative method is needed for quality and authenticity evaluation of civet coffee. A rapid, reliable and cost-effective analysis employing a universal detector, GC coupled with flame ionization detector (FID), and metabolite fingerprinting has been established for discrimination analysis of 37 commercial and non-commercial coffee beans extracts. gas chromatography/flame ionization detector (GC/FID) provided higher sensitivity over a similar range of detected compounds than GC/MS. In combination with multivariate analysis, GC/FID could successfully reproduce quality prediction from GC/MS for differentiation of commercial civet coffee, regular coffee and coffee blend with 50 wt % civet coffee content without prior metabolite details. Our study demonstrated that GC/FID-based metabolite fingerprinting can be effectively actualized as an alternative method for coffee authenticity screening in industries.

Metabolomics and diabetes: analytical and computational approaches

Diabetes is characterized by altered metabolism of key molecules and regulatory pathways. The phenotypic expression of diabetes and associated complications encompasses complex interactions between genetic, environmental, and tissue-specific factors that require an integrated understanding of perturbations in the network of genes, proteins, and metabolites. Metabolomics attempts to systematically identify and quantitate small molecule metabolites from biological systems. The recent rapid development of a variety of analytical platforms based on mass spectrometry and nuclear magnetic resonance have enabled identification of complex metabolic phenotypes. Continued development of bioinformatics and analytical strategies has facilitated the discovery of causal links in understanding the pathophysiology of diabetes and its complications. Here, we summarize the metabolomics workflow, including analytical, statistical, and computational tools, highlight recent applications of metabolomics in diabetes research, and discuss the challenges in the field.

Prediction of intracellular metabolic states from extracellular metabolomic data

Metabolic models can provide a mechanistic framework to analyze information-rich omics data sets, and are increasingly being used to investigate metabolic alternations in human diseases. An expression of the altered metabolic pathway utilization is the selection of metabolites consumed and released by cells. However, methods for the inference of intracellular metabolic states from extracellular measurements in the context of metabolic models remain underdeveloped compared to methods for other omics data. Herein, we describe a workflow for such an integrative analysis emphasizing on extracellular metabolomics data. We demonstrate, using the lymphoblastic leukemia cell lines Molt-4 and CCRF-CEM, how our methods can reveal differences in cell metabolism. Our models explain metabolite uptake and secretion by predicting a more glycolytic phenotype for the CCRF-CEM model and a more oxidative phenotype for the Molt-4 model, which was supported by our experimental data. Gene expression analysis revealed altered expression of gene products at key regulatory steps in those central metabolic pathways, and literature query emphasized the role of these genes in cancer metabolism. Moreover, in silico gene knock-outs identified unique control points for each cell line model, e.g., phosphoglycerate dehydrogenase for the Molt-4 model. Thus, our workflow is well-suited to the characterization of cellular metabolic traits based on extracellular metabolomic data, and it allows the integration of multiple omics data sets into a cohesive picture based on a defined model context.

Invariance and plasticity in the Drosophila melanogaster metabolomic network in response to temperature

Background

Metabolomic responses to extreme thermal stress have recently been investigated in Drosophila melanogaster. However, a network level understanding of metabolomic responses to longer and less drastic temperature changes, which more closely reflect variation in natural ambient temperatures experienced during development and adulthood, is currently lacking. Here we use high-resolution, non-targeted metabolomics to dissect metabolomic changes in D. melanogaster elicited by moderately cool (18°C) or warm (27°C) developmental and adult temperature exposures.

Results

We find that temperature at which larvae are reared has a dramatic effect on metabolomic network structure measured in adults. Using network analysis, we are able to identify modules that are highly differentially expressed in response to changing developmental temperature, as well as modules whose correlation structure is strongly preserved across temperature.

Conclusions

Our results suggest that the effect of temperature on the metabolome provides an easily studied and powerful model for understanding the forces that influence invariance and plasticity in biological networks.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-014-0139-6) contains supplementary material, which is available to authorized users.

Autophagy is essential for effector CD8(+) T cell survival and memory formation

The importance of autophagy in the generation of memory CD8(+) T cells in vivo is not well defined. We report here that autophagy was dynamically regulated in virus-specific CD8(+) T cells during acute infection of mice with lymphocytic choriomeningitis virus. In contrast to the current paradigm, autophagy decreased in activated proliferating effector CD8(+) T cells and was then upregulated when the cells stopped dividing just before the contraction phase. Consistent with those findings, deletion of the gene encoding either of the autophagy-related molecules Atg5 or Atg7 had little to no effect on the proliferation and function of effector cells, but these autophagy-deficient effector cells had survival defects that resulted in compromised formation of memory T cells. Our studies define when autophagy is needed during effector and memory differentiation and warrant reexamination of the relationship between T cell activation and autophagy.

WITHDRAWN: Recent advances in chemometric methods for plant metabolomics: A review

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Comparative transcriptomics and metabolomics in a rhesus macaque drug administration study

We describe a multi-omic approach to understanding the effects that the anti-malarial drug pyrimethamine has on immune physiology in rhesus macaques (Macaca mulatta). Whole blood and bone marrow (BM) RNA-Seq and plasma metabolome profiles (each with over 15,000 features) have been generated for five naïve individuals at up to seven timepoints before, during and after three rounds of drug administration. Linear modeling and Bayesian network analyses are both considered, alongside investigations of the impact of statistical modeling strategies on biological inference. Individual macaques were found to be a major source of variance for both omic data types, and factoring individuals into subsequent modeling increases power to detect temporal effects. A major component of the whole blood transcriptome follows the BM with a time-delay, while other components of variation are unique to each compartment. We demonstrate that pyrimethamine administration does impact both compartments throughout the experiment, but very limited perturbation of transcript or metabolite abundance was observed following each round of drug exposure. New insights into the mode of action of the drug are presented in the context of pyrimethamine's predicted effect on suppression of cell division and metabolism in the immune system.

Effects of age, sex, and genotype on high-sensitivity metabolomic profiles in the fruit fly, Drosophila melanogaster

Researchers have used whole-genome sequencing and gene expression profiling to identify genes associated with age, in the hope of understanding the underlying mechanisms of senescence. But there is a substantial gap from variation in gene sequences and expression levels to variation in age or life expectancy. In an attempt to bridge this gap, here we describe the effects of age, sex, genotype, and their interactions on high-sensitivity metabolomic profiles in the fruit fly, Drosophila melanogaster. Among the 6800 features analyzed, we found that over one-quarter of all metabolites were significantly associated with age, sex, genotype, or their interactions, and multivariate analysis shows that individual metabolomic profiles are highly predictive of these traits. Using a metabolomic equivalent of gene set enrichment analysis, we identified numerous metabolic pathways that were enriched among metabolites associated with age, sex, and genotype, including pathways involving sugar and glycerophospholipid metabolism, neurotransmitters, amino acids, and the carnitine shuttle. Our results suggest that high-sensitivity metabolomic studies have excellent potential not only to reveal mechanisms that lead to senescence, but also to help us understand differences in patterns of aging among genotypes and between males and females.

After the feature presentation: technologies bridging untargeted metabolomics and biology

Liquid chromatography/mass spectrometry-based untargeted metabolomics is now an established experimental approach that is being broadly applied by many laboratories worldwide. Interpreting untargeted metabolomic data, however, remains a challenge and limits the translation of results into biologically relevant conclusions. Here we review emerging technologies that can be applied after untargeted profiling to extend biological interpretation of metabolomic data. These technologies include advances in bioinformatic software that enable identification of isotopes and adducts, comprehensive pathway mapping, deconvolution of MS(2) data, and tracking of isotopically labeled compounds. There are also opportunities to gain additional biological insight by complementing the metabolomic analysis of homogenized samples with recently developed technologies for metabolite imaging of intact tissues. To maximize the value of these emerging technologies, a unified workflow is discussed that builds on the traditional untargeted metabolomic pipeline. Particularly when integrated together, the combination of the advances highlighted in this review helps transform lists of masses and fold changes characteristic of untargeted profiling results into structures, absolute concentrations, pathway fluxes, and localization patterns that are typically needed to understand biology.

Systems biological approaches to measure and understand vaccine immunity in humans

Recent studies have demonstrated the utility of using systems approaches to identify molecular signatures that can be used to predict vaccine immunity in humans. Such approaches are now being used extensively in vaccinology, and are beginning to yield novel insights about the molecular networks driving vaccine immunity. In this review, we present a broad review of the methodologies involved in these studies, and discuss the promise and challenges involved in this emerging field of "systems vaccinology."