O-PLS & Metabonomics

8/8/2019 O-PLS & Metabonomics

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By: Parvaneh EbrahimiBy: Parvaneh Ebrahimi

JulyJuly--20102010


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Recently, postgenomic technologies, including transcriptomic andproteomic methods, have been developing as a means of generating

high information content biological assays for disease diagnoses or for

evaluating the beneficial or adverse effects of pharmaceuticals

INTRODUCTIONINTRODUCTION


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MetabonomicsMetabonomics is an emerging complementary postgenomic technology

and involves the determination of the levels and time dependence of

low molecular mass endogenous metabolites in biofluids such as

urine, plasma, or in tissues, as a result of some pathophysiological

effect.

Most studies have used 1H nuclear magnetic resonance spectroscopy

(1H NMR) for data generation, but more recently, liquid

chromatography hyphenated with mass spectrometry, LC/MS, has

been employed.

In other types of biological systems, GC/MS has been used extensively.


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Megavariate pattern recognition methods are used to extract relevantbiological information from the complex data.

Unsupervised or supervised chemometrics methods, such as principal

component analysis (PCA) or discriminant analysis by projection

on latent structures (PLS-DA), are often used.

In HNMR, amount of the data is huge and peaks corresponding to

certain molecules, such as citrate, can be subject to peak positionvariation from sample to sample. The chief cause of such

variability is pH differences between samples.

.


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Methods that reduce the amount of data down to manageableproportions have been used.

One of the most common methods involves signal integration within

spectral regions orb

ins , and these have generally correspondedto a typical spectral width of 0.04 ppm.

This data reduction method also has the property of largely

concealing any peak position variation and thus providing more

robust modeling.


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Pattern RecognitionPattern Recognition

The multivariate pattern recognition method used in this paper is

O-PLS, written as:

StructuredNoise


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For the applications presented here, each line of the X matrix is an

NMR spectrum corresponding to one sample and each column

of Y defines a class (or group) whose values are dummy variables.

The method can therefore be defined as O-PLS-DA.

The O-PLS loading and regression coefficients allow for a morerealistic interpretation than PLS, because in the O-PLS,

the structured noise is modeled separately from the variation

common toX and Y .


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To test the validity of models against over-fitting, the cross-validation

parameter was computed:


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EXPERIMENTAL SECTIONEXPERIMENTAL SECTION

To 62 male Sprague-Dawley rats ,saline(0.9% ) alone or with

was administrated.1-mg.kg75.0at a dose level of2HgCl

Urine samples were collected continuously at various time intervals:

(predose and 0-8, 8-24, 24-32, 32-48, 48-72, 72-96, 96-120, 120-144,and 144-168 hr after treatment).

To minimize any gross variation in the pH of the urine samples,

Phosphate buffer solution was used to fix the samples PH in (7.4).

Urine samples were centrifuged in order to remove particulatecontaminants, and the samples were stored at -40 C pending NMRspectroscopic analysis.


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For each sample, an 1H NMR spectrum was measured at 600.13MHz

on a BrukerDRX-600 spectrometer.

To make a comparison of the use of real NMR spectra and reduced

spectra, the original binning methodology was also applied, andhere each NMR spectrum was segmented into 250 chemical shift

regions of 0.04 ppm.

Finally, to take account of large variations in urine concentration, using

both reduced and full resolution data sets, all spectra were then

normalized.


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After data acquisition, the spectra of the 62 rat urine samples were

separated into two groups corresponding to the control and treatedgroups and finally an O-PLS model is generated.


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Citrate is known to be one of the compounds that have the largest peak

position variation, mainly due to pH and metal ion variation.

It is obvious that the impact of this peak position variation on pattern

recognition models must be important.


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SIMULATIONSIMULATIONA simulation study based on the variation of only three compounds that

are subject to peak position variation may provide the information

necessary for accurate interpretation of more complex data sets such

as in a full toxicological study.

A biofluid NM

R spectrum can be considered as a linear combination ofspectra of pure single molecule components.


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several molecules were chosen as chemical compounds of interest

because of their known peak position variation or overlap. These were

citrate, taurine, and trimethylamine N-oxide (TMAO).The last two molecules havebeen chosen not only because their

respective signals are subject to positional noise but also because they

overlap, a common feature of real NMR spectra.


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without peak position variationwithout peak position variation


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molecule prone to a peak position variation but which is notmolecule prone to a peak position variation but which is not

related to the discrimination between the two groupsrelated to the discrimination between the two groups


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variation which is related to thevariation which is related to the

discrimination between the twodiscrimination between the twogroupsgroups


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CONCLUSIONCONCLUSION

In this paper, it was demonstrated that metabonomic studies can be

carried out without any reduction of the true 1H NMR spectral

resolution.

From simulation studies, variation of peak position can reduce slightly

the prediction ability of the model, but at the same time , this peak

position variation can be modeled by the pattern recognition

method and therefore may provide:

useful information about physicochemical variations in the

biofluid Matrix.


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CONCLUSIONCONCLUSION

For any other analytical techniques (i.e., LC-MS), where peak position

variation is often considered as problematic, it may also be the case.

The complexity of the loading interpretation due to the high number of

variables can be simplified using O-PLS, which reduces the number

of loadings to be interpreted.


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THANK YOUTHANK YOU

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O-PLS & Metabonomics

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