Helen L. Kotze

Influence of Missing Values Substitutes on Multivariate Analysis of Metabolomics Data

Missing values are known to be problematic for the analysis of gas chromatography-mass spectrometry (GC-MS) metabolomics data. Typically these values cover about 10%–20% of all data and can originate from various backgrounds, including analytical, computational, as well as biological. Currently, the most well known substitute for missing values is a mean imputation. In fact, some researchers consider this aspect of data analysis in their metabolomics pipeline as so routine that they do not even mention using this replacement approach. However, this may have a significant influence on the data analysis output(s) and might be highly sensitive to the distribution of samples between different classes. Therefore, in this study we have analysed different substitutes of missing values namely: zero, mean, median, k-nearest neighbours (kNN) and random forest (RF) imputation, in terms of their influence on unsupervised and supervised learning and, thus, their impact on the final output(s) in terms of biological interpretation. These comparisons have been demonstrated both visually and computationally (classification rate) to support our findings. The results show that the selection of the replacement methods to impute missing values may have a considerable effect on the classification accuracy, if performed incorrectly this may negatively influence the biomarkers selected for an early disease diagnosis or identification of cancer related metabolites. In the case of GC-MS metabolomics data studied here our findings recommend that RF should be favored as an imputation of missing value over the other tested methods. This approach displayed excellent results in terms of classification rate for both supervised methods namely: principal components-linear discriminant analysis (PC-LDA) (98.02%) and partial least squares-discriminant analysis (PLS-DA) (97.96%) outperforming other imputation methods.

A novel untargeted metabolomics correlation-based network analysis incorporating human metabolic reconstructions

BackgroundMetabolomics has become increasingly popular in the study of disease phenotypes and molecular pathophysiology. One branch of metabolomics that encompasses the high-throughput screening of cellular metabolism is metabolic profiling. In the present study, the metabolic profiles of different tumour cells from colorectal carcinoma and breast adenocarcinoma were exposed to hypoxic and normoxic conditions and these have been compared to reveal the potential metabolic effects of hypoxia on the biochemistry of the tumour cells; this may contribute to their survival in oxygen compromised environments. In an attempt to analyse the complex interactions between metabolites beyond routine univariate and multivariate data analysis methods, correlation analysis has been integrated with a human metabolic reconstruction to reveal connections between pathways that are associated with normoxic or hypoxic oxygen environments.ResultsCorrelation analysis has revealed statistically significant connections between metabolites, where differences in correlations between cells exposed to different oxygen levels have been highlighted as markers of hypoxic metabolism in cancer. Network mapping onto reconstructed human metabolic models is a novel addition to correlation analysis. Correlated metabolites have been mapped onto the Edinburgh human metabolic network (EHMN) with the aim of interlinking metabolites found to be regulated in a similar fashion in response to oxygen. This revealed novel pathways within the metabolic network that may be key to tumour cell survival at low oxygen. Results show that the metabolic responses to lowering oxygen availability can be conserved or specific to a particular cell line. Network-based correlation analysis identified conserved metabolites including malate, pyruvate, 2-oxoglutarate, glutamate and fructose-6-phosphate. In this way, this method has revealed metabolites not previously linked, or less well recognised, with respect to hypoxia before. Lactate fermentation is one of the key themes discussed in the field of hypoxia; however, malate, pyruvate, 2-oxoglutarate, glutamate and fructose-6-phosphate, which are connected by a single pathway, may provide a more significant marker of hypoxia in cancer.ConclusionsMetabolic networks generated for each cell line were compared to identify conserved metabolite pathway responses to low oxygen environments. Furthermore, we believe this methodology will have general application within metabolomics.

Metabolic profiling reveals potential metabolic markers associated with Hypoxia Inducible Factor-mediated signalling in hypoxic cancer cells

Hypoxia inducible factors (HIFs) plays an important role in oxygen compromised environments and therefore in tumour survival. In this research, metabolomics has been applied to study HIFs metabolic function in two cell models: mouse hepatocellular carcinoma and human colon carcinoma, whereby the metabolism has been profiled for a range of oxygen potentials. Wild type cells have been compared to cells deficient in HIF signalling to reveal its effect on cellular metabolism under normal oxygen conditions as well as low oxygen, hypoxic and anoxic environments. Characteristic responses to hypoxia that were conserved across both cell models involved the anti-correlation between 2-hydroxyglutarate, 2-oxoglutarate, fructose, hexadecanoic acid, hypotaurine, pyruvate and octadecenoic acid with 4-hydroxyproline, aspartate, cysteine, glutamine, lysine, malate and pyroglutamate. Further to this, network-based correlation analysis revealed HIF specific pathway responses to each oxygen condition that were also conserved between cell models. From this, 4-hydroxyproline was revealed as a regulating hub in low oxygen survival of WT cells while fructose appeared to be in HIF deficient cells. Pathways surrounding these hubs were built from the direct connections of correlated metabolites that look beyond traditional pathways in order to understand the mechanism of HIF response to low oxygen environments.

Imaging of metabolites using secondary ion mass spectrometry

This article provides an overview of the technique of secondary ion mass spectrometry imaging and highlights some current and future areas of application relevant to the field of metabolomics. The approach benefits from label-free analysis of molecular species up to ~1500 Da with minimal sample preparation. Offering the highest spatial resolution of current mass spectrometry imaging methodologies, the technique is well-suited to metabolite imaging in both biological tissue and cells, in both 2D and 3D.

Correlation-based network analysis of cancer metabolism

Genetically uniform cultivars in many self-pollinated cereal crops dominate commercial production in high-input environments especially due to their high grain yields and wide geographical adaptation. These cultivars generally perform well under favorable and high-input farming systems but their optimal performance cannot be achieved on marginal/organic lands or without the use of external chemical inputs (fertilizers, herbicides and pesticides). Cereal breeding programs aim at evaluating candidate lines/cultivars for agronomic, disease and quality traits in a weed free environment that makes it impossible to identify traits conferring competitive ability against weeds. Moreover, quantification of competitive ability is a complex phenomenon which is affected by range of growth traits.

Metabolic Fingerprinting of In Vitro Cancer Cell Samples

Metabolomics is a commonly used tool in systems biology. Since a range of metabolites can be detected in a single assay, metabolomics can be defined as a holistic and data-driven study of the low molecular weight metabolites present in biological systems (Dunn, Phys Biol 5(1):11001, 2008). The metabolome consists of endogenous and exogenous components: those catabolised or anabolised by the cell or organism itself, or those that are extra-organism or extracellular respectively. The metabolome includes metabolites present in a cell or organism that participate in metabolic reactions required for growth, maintenance and function, as well as metabolites consumed from the external environment. If considering an organism in vivo, the external environment could include the metabolomes of interacting organisms, for example from gut microflora in humans (Dunn, Phys Biol 5(1):11001, 2008). In in vitro metabolomics (as presented in this research), the external environment is considered the growth medium. Although the functional levels of a biological system include the genome, transcriptome, proteome and metabolomes, the latter is considered most representative of the phenotype (Dunn, Phys Biol 5(1):11001, 2008). Exploring the metabolome following experimental perturbation, where subtle changes can be tractable, may be the best way to reveal the phenotypic changes relative to biological function. For these reasons metabolomics is one of the fastest developing disciplines in systems biology and other aspects of modern science.

Cancer Hypoxia and the Tumour Microenvironment as Effectors of Cancer Metabolism

Mammalian cells have various control mechanisms that regulate homeostasis, the maintenance of a constant cellular environment. This includes regulating oxygen homeostasis, such that the need for oxygen during oxidative phosphorylation and other metabolic reactions is balanced with the risk of oxidative damage within the cell, as reported by Ruan et al. (J Cell Biochem 107(6), 1053–1062, 2009). Hypoxia is the intermediate state between the homeostatic state of normoxia and the complete absence of oxygen in anoxia. Under hypoxia, the survival of a cell, tissue, organ or organism is governed by its ability to detect and respond to oxygen availability and mount an adaptive response facilitating tolerance of the oxygen deprivation.

Network-Based Correlation Analysis of Metabolic Fingerprinting Data

Correlation analysis, first invented by Francis Galton and later scientifically conceptualised by Karl Pearson, has many powerful applications in biology for describing causality in biological systems. Ever since the 1920s, causation has been connected with correlation in this way. The underlying mechanisms in biological processes are shadowed in correlations that when analysed can reveal connections in biological data that provide a starting point to realise underlying biological processes.

An Overview of Cancer Metabolism

The metabolome is considered the closest entity to the phenotype of a biological system. It displays the changes made at higher hierarchical levels such as the proteome, transcriptome and genome. For many diseases including cancer, studying the metabolome enables us to gain a better understanding of global biological response of cancer cells in the progression of the disease. Revealing the complexity of the metabolome is particularly advantageous to understand the phenotypic function of a cancer cell that is governed by the preceding levels (proteins, transcription factors and genes).

Case Study: Systems Biology of HIF Metabolism in Cancer

Hypoxia and HIFs (particularly the overexpression of HIF-1) are associated with chemotherapy and radiotherapy resistance (Ruan et al. J Cell Biochem 107(6):1053–1062, 2009), thus they play a critical role in tumour survival and defence against eradication. Our knowledge and understanding of the mechanisms of HIFs have started to illustrate great scope in designing and screening of new anticancer therapies (Ruan et al. J Cell Biochem 107(6):1053–1062, 2009; Semenza Trends Pharmacol Sci 33(4):207–214, 2012). However, simply developing antagonists to the HIF pathway is not enough as it is not yet established that HIF drives the transformation of a normal cell to a cancer cell (Esteban and Maxwell Nat Med 11(10):1047–1048, 2005).