on 04-Mar-2021 (Thu)

Each ANN features at least one input layer, which propagates a set of values through one or multiple hidden layers after which the output layer forms the final network output.

By measuring and mathematically modelling changes in the levels of products of metabolism found in biological fluids and tissues, metabonomics offers fresh insight into the effects of diet, drugs and disease

Metabonomics, and the related field of metabolomics, uses modern techniques to analyse samples, but the basic principle of relating chemical patterns to biology is the same

Metabonomics broadly aims to meas- ure the global, dynamic metabolic response of living systems to biological stimuli or genetic manipula- tion. The focus is on understanding sys- temic change through time in complex multi cellular systems. Metabolomics seeks an analytical descrip- tion of complex biologi- cal samples, and aims to characterize and quantify all the small molecules in such a sample. In practice, the terms are often used interchange- ably, and the analytical and modelling procedures are the same.

But metabonomics really took off with the realization that pattern-recognition methods (also known as chemometrics or multi variate statistical analysis) could help to interpret the complex data sets that result from these studies

A problem with systems biology is that each level of biological organization and control — genomics, gene expres- sion, protein expression and metabolism — oper- ates on a markedly dif- ferent timescale from the others, making it difficult to find causal linkages

Metabonomics cuts through these problems by monitoring the global outcome of all the influ- encing factors, without making assumptions about the effect of any single contribution to that outcome. Yet in so doing, the individual contributions can be teased out

Usually NMR spectroscopy and MS. NMR is generally used to detect hydrogen atoms in metabolites. In a typical biological-fluid sample, all hydrogen-containing molecules in the sample — including nearly all metabolites — will give an 1 H NMR spectrum, as long as they are present in concentrations above the detection limit.

MS studies, on the other hand, usually require the metabolites to be separated from the biologi- cal fluid before detection, typically by using high-performance liquid chromatography (or modern variants).

Furthermore, both can be used to identify the metabolites’ structures, and to measure the relative and absolute concentra- tions of the molecules (although NMR is more reliable for determining concentrations).

Because NMR doesn’t dam- age analytes, it is particularly useful for study- ing metabolite levels in intact tissues, such as biopsy samples, which can then be used in further experiments.

The spectra of samples from organisms of interest (such as those with a specific disease) are compared with those from controls, so that the spectral features caused by the dis- ease state can be determined. Precise metabo- lite concentrations are not always necessary to formulate hypotheses about the mechanism of the disease. But if only a few metabolites turn out to be important, then knowledge of their concentrations might be instructive, and these can subsequently be measured

Doesn’t the quantity and complexity of the data make interpretation impossible? Not if you use multivariate statistical analy- sis — a collection of techniques in which the intensities of peaks in a spectrum are used as coordinates in multidimensional plots of metabolic activity.

A widely used supervised method is called par- tial least squares (PLS) analysis. PLS is often combined with discriminant analysis — a method that finds a linear combination of fea- tures that can be used to classify data into sets. The combination of PLS with discriminant analysis defines a surface that can be placed in a multidimensional plot to separate data into classes

There are various methods based on NMR and MS, but one exciting new approach is an NMR technique termed statistical total correlation spectroscopy (STOCSY). In this method, cor- relations are found between the intensities of spectral peaks across a cohort of samples. This enables all the peaks from a given metabolite to be identified, so that the structure of that molecule can be determined.

They can be used as diagnostic biomarkers for biological events. Metabonomics thus allows real-world, medical observations to be related to data from all the other ‘-omics’ technologies, which are less directly related to actual bio- logical outcomes than metabolism is (Fig. 3, overleaf).

It has also been demonstrated in animals that the metabolic profile of an individual’s urine can be used to predict both how that individual will metabolize a given drug and their susceptibility to the side effects of that drug. If this principle can be applied widely in humans, it will have enormous implications for personalized health care and in optimizing clinical trials.

Metabolites have also been found that act as indicators for disease risk, individual susceptibility, or as markers of recovery from an illness.

What will be the next big thing in metabonomics? We believe it will be metabolome-wide associ- ation (MWA) studies, which identify relation- ships between metabolic profiles and disease risks for both individuals and populations. In this approach, the metabolic profiles of thou- sands of people are captured spectroscopically, and are then statistically linked to disease risk factors such as obesity and diabetes.

Metabolomics is the study of the raw mate- rials and products of the body’s biochemical reactions, molecules that are smaller than most proteins, DNA and other macromolecules

Small changes in the activity of a gene or protein (which may have an unknown impact on the workings of a cell) often cre- ate a much larger change in metabolite levels.

There is also no clear division between compounds produced by the human body, those produced by our gut bacteria and fleeting products gener- ated by food or drugs swallowed that day

An added complication is that one person’s profile of metabolites is likely to be dramati- cally different from another’s, and each may fluctuate markedly depending on the time of day, what they last ate and other aspects of their lifestyle. To get a handle on this varia- tion, Nicholson has studied tens of thousands of urine samples from many ethnic groups around the world and found that each group is remarkably different

A separate study showed that the metabolic profile of meat eaters is very different from that of vegetarians 2 .

Nicholson and his colleagues showed that a fingerprint of the metabolites in urine could predict which rats would suffer liver damage from the drug paracetamol 3 .

They will also have to learn from those work- ing on gene-expression or proteomic profiles, who have sometimes struggled to show that a test that works in one group also works in another, or that the changes they see are actu- ally involved in a disease. “In my opinion we should know why this metabolite is going up or down,” says William Bigbee, an expert in biomarkers and proteomics at the University of Pittsburgh Cancer Institute, Philadelphia

In particu- lar, genome-wide association studies (GWAS) enable genetic features to be linked to many pre-disease conditions (Kruglyak, 2008). But GWAS can explain only a small proportion of incidence variation and may not translate between different populations—for example, hav- ing two copies of an FTO gene variant is associated with significantly higher levels of obesity and type 2 diabetes in Western populations but not in the Han Chinese (Li et al., 2008a).

The metabotype or metabolic pheno- type (Box 1) provides a readout of the metabolic state of an individual and is the product of genetic and environmen- tal (diet, lifestyle, gut microbial activity) contributions under a particular set of conditions (Nicholson, 2006)

Such metabolic profiles provide infor- mation that cannot be obtained directly from the genotype, gene expression pro- files, or even the proteome of an individ- ual.

Factors such as gender, age, diet, gut microbiota, physical activity, latent dis- ease, medication, hormone, and stress levels modify the metabotypes of both individuals and populations (Figure 1), and hence the prevalence and risk of disease (Nicholson, 2006; Daviglus et al., 2004; Li et al., 2008b).

Metabolome- wide association studies (MWAS) have the capacity to link human metabotype variations to disease risk factors in the general population (Holmes et al., 2008).

Clearly, in a clinical setting, the complex analytical instrumentation used in the discovery phase of this type of work would not be appropriate or affordable for the mea- surement of these markers in patients. Implementation of this type of monitor- ing will depend on evolving technolo- gies such as “lab-on-a-chip” methods, sensors and electronic “noses,” which can detect complex molecular finger- prints (Westhof et al. 2007) that can be tuned to detect specific biomarkers (or pathways) rather than providing compre- hensive metabolic profiles.

An emerging area that illustrates how this might be achieved is in breath analysis using ion mobility spectrometry (IMS), which has shown promise for the diagnosis of infections, lung cancer and sarcoidosis based on detecting a range of low molecular weight volatile metabo- lites in exhaled breath (Westhof et al., 2007)

In “top-down” systems biology we study how biocom- partments (subcellular, cellular, tissue, organ) interact in space and time using minimally invasive methods that capture the properties of systemic homeostasis and its dysregulation (Nicholson, 2006).

Of particular inter- est are methods that investigate gene- gene interactions directly by analyzing and interpreting microarray expression data against a background of traditional genetic mapping techniques to estab- lish associations between quantitative trait loci and expression data (eQTLs).

Three susceptibility genes for obesity were identified by an eQTL approach that predicted transcriptional responses to a single gene perturbation in mice (Schadt et al., 2005). This principle was subse- quently extended to the use of metabolic profiles (mQTL) as the basis for mapping loci associated with quantitative changes in the plasma of a rat strain susceptible to type 2 diabetes (Dumas et al., 2007)

It is important to elucidate how different levels of cellular nanomachinery interact in space and time to control metabolism and how they are disrupted in disease. A variety of advanced mathematical tools including linear projection or Bayes- ian (conditional probabilistic) methods with bidirectional orthogonal filters (to remove unwanted noise components in the data) can be used to identify co- variation between metabolite profiles and transcriptomic, proteomic, or meta- genomic data matrices and so give new insight into multilevel interactions in whole organisms and how they relate to disease (Nicholson, 2006)

We used a projection-to-latent structure (PLS) modeling method to predict liver toxicity in rats given a threshold toxic dose of the analgesic acetaminophen. There was a significant association between the pre-dose metabolite profile in rat urine and the post-dose outcome regarding urinary excretion of acetamin- ophen metabolites and the severity of liver damage

The goal of MWAS is to perform large- scale metabotyping of populations using spectroscopic methods and to relate these metabotypes to disease risk fac- tors.

This has been demonstrated in human populations: a biomarker (for- mate) identified in the human urinary metabolome inversely correlates with blood pressure (Holmes et al., 2008). Dietary salt intake is strongly linked to blood pressure; formate is involved in chloride-ion exchange in the kidney via the chloride-formate exchanger and the SLC26 transporters (a series of intercon- nected renal ion exchangers involved in Na + and Cl − transport) (Sindic et al., 2007).

Thus, MWAS stud- ies have the potential to provide new insights into disease mechanisms and pathophysiology that may ultimately lead to new drug targets